Heidi.Data.Frame.Algorithms.GenericTrie

Contents

Description

Synopsis

unionColsWith :: (Eq k, TrieKey k) => (v -> v -> v) -> Frame (Row k v) -> Frame (Row k v) -> Frame (Row k v)
spreadWith :: (TrieKey k, Foldable t, Ord k, Ord v) => (v -> k) -> k -> k -> t (Row k v) -> Frame (Row k v)
gatherWith :: (Foldable t, Ord k, TrieKey k) => (k -> v) -> Set k -> k -> k -> t (Row k v) -> Frame (Row k v)
groupBy :: (Foldable t, TrieKey k, Eq k, Ord v) => k -> t (Row k v) -> Map v (Frame (Row k v))
innerJoin :: (Foldable t, Ord v, TrieKey k, Eq v, Eq k) => k -> k -> t (Row k v) -> t (Row k v) -> Frame (Row k v)
leftOuterJoin :: (Foldable t, Ord v, TrieKey k, Eq v, Eq k) => k -> k -> t (Row k v) -> t (Row k v) -> Frame (Row k v)

Row-wise operations

Arguments

:: (Eq k, TrieKey k)
=> (v -> v -> v)	Element combination function
-> Frame (Row k v)
-> Frame (Row k v)
-> Frame (Row k v)

Merge two frames by taking the set union of the columns

Filtering

Arguments

:: (TrieKey k, Foldable t, Ord k, Ord v)
=> (v -> k)
-> k	"key" key
-> k	"value" key
-> t (Row k v)	input dataframe
-> Frame (Row k v)

spreadWith moves the unique values of a key column into the column names, spreading the values of a value column across the new columns.

Arguments

gatherWith moves column names into a "key" column, gathering the column values into a single "value" column

Arguments

:: (Foldable t, TrieKey k, Eq k, Ord v)
=> k	Key to group by
-> t (Row k v)	A 'Frame (GTR.Row k v) can be used here
-> Map v (Frame (Row k v))

GROUP BY : given a key and a table that uses it, split the table in multiple tables, one per value taken by the key.

>>> numRows <$> (HM.lookup "129" $ groupBy "id.0" t0)
Just 2

Arguments

:: (Foldable t, Ord v, TrieKey k, Eq v, Eq k)
=> k	Key into the first table
-> k	Key into the second table
-> t (Row k v)	First dataframe
-> t (Row k v)	Second dataframe
-> Frame (Row k v)

INNER JOIN : given two dataframes and one key from each, compute the inner join using the keys as relations.

>>> head t0
[("id.0","129"),("qty","1"),("item","book")]

>>> head t1
[("id.1","129"),("price","100")]

>>> head $ innerJoin "id.0" "id.1" t0 t1
[("id.1","129"),("id.0","129"),("qty","5"),("item","book"),("price","100")]

leftOuterJoin :: (Foldable t, Ord v, TrieKey k, Eq v, Eq k) => k -> k -> t (Row k v) -> t (Row k v) -> Frame (Row k v) Source #

LEFT (OUTER) JOIN : given two dataframes and one key from each, compute the left outer join using the keys as relations.